{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#coding=utf-8\n",
    "#############in MLia chp10 For KMeans\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import * \n",
    "#import kMeans\n",
    "import time\n",
    "\n",
    "import os \n",
    "os.chdir(\"/home/lab466/pythons/pyMLIA35/Ch10Kmeans\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load data\n",
    "def loadDataSet(fileName):\n",
    "    dataMat = []\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines(): #for each line\n",
    "        curLine = line.strip().split('\\t')\n",
    "        fltLine = list(map(float,curLine)) #这里和书中不同 和上一章一样修改\n",
    "        dataMat.append(fltLine)\n",
    "    return dataMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#distance func\n",
    "def distEclud(vecA,vecB):\n",
    "    return sqrt(sum(power(vecA - vecB, 2)))  # la.norm(vecA-vecB) 向量AB的欧式距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#init K points randomly\n",
    "def randCent(dataSet, k):\n",
    "    n = shape(dataSet)[1]\n",
    "    centroids = mat(zeros((k,n)))#create centroid mat\n",
    "    for j in range(n):#create random cluster centers, within bounds of each dimension\n",
    "        minJ = min(dataSet[:,j])\n",
    "        rangeJ = float(max(dataSet[:,j]) - minJ)\n",
    "        centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#K-均值算法:\n",
    "def kMeans(dataSet,k,distMeas=distEclud,createCent=randCent):\n",
    "    #参数：dataset,num of cluster,distance func,initCen\n",
    "    m=shape(dataSet)[0]\n",
    "    clusterAssment=mat(zeros((m,2)))#store the result matrix,2 cols for index and error\n",
    "    centroids=createCent(dataSet,k)\n",
    "    clusterChanged=True\n",
    "    while clusterChanged:\n",
    "        clusterChanged=False\n",
    "        for i in range(m):#for every points\n",
    "            minDist = inf;minIndex = -1#init\n",
    "            for j in range(k):#for every k centers，find the nearest center\n",
    "                distJI=distMeas(centroids[j,:],dataSet[i,:])\n",
    "                if distJI<minDist:#if distance is shorter than minDist\n",
    "                    minDist=distJI;minIndex=j# update distance and index(类别)\n",
    "            if clusterAssment[i,0] != minIndex:\n",
    "                clusterChanged = True\n",
    "                #此处判断数据点所属类别与之前是否相同（是否变化，只要有一个点变化就重设为True，再次迭代）\n",
    "            clusterAssment[i,:] = minIndex,minDist**2\n",
    "        #print(centroids)\n",
    "        # update k center\n",
    "        for cent in range(k):\n",
    "            ptsInClust=dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]\n",
    "            centroids[cent,:]=mean(ptsInClust,axis=0)\n",
    "    return centroids,clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#二分K-均值聚类\n",
    "def biKmeans(dataSet,k,distMeas=distEclud):\n",
    "    m=shape(dataSet)[0]\n",
    "    clusterAssment=mat(zeros((m,2)))\n",
    "    centroid0 = mean(dataSet, axis=0).tolist()[0]\n",
    "    centList = [centroid0]  # create a list with one centroid\n",
    "    for j in range(m):  # calc initial Error for each point\n",
    "        clusterAssment[j, 1] = distMeas(mat(centroid0), dataSet[j, :]) ** 2\n",
    "    while (len(centList) < k):\n",
    "        lowestSSE = inf #init SSE\n",
    "        for i in range(len(centList)):#for every centroid\n",
    "            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:, 0].A == i)[0],:]  # get the data points currently in cluster i\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)# k=2,kMeans\n",
    "            sseSplit = sum(splitClustAss[:, 1])  # compare the SSE to the currrent minimum\n",
    "            sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:, 0].A != i)[0], 1])\n",
    "            print(\"sseSplit, and notSplit: \", sseSplit, sseNotSplit)\n",
    "            if (sseSplit + sseNotSplit) < lowestSSE: #judge the error\n",
    "                bestCentToSplit = i\n",
    "                bestNewCents = centroidMat\n",
    "                bestClustAss = splitClustAss.copy()\n",
    "                lowestSSE = sseSplit + sseNotSplit\n",
    "        #new cluster and split cluster\n",
    "        bestClustAss[nonzero(bestClustAss[:, 0].A == 1)[0], 0] = len(centList)  # change 1 to 3,4, or whatever\n",
    "        bestClustAss[nonzero(bestClustAss[:, 0].A == 0)[0], 0] = bestCentToSplit\n",
    "        print('the bestCentToSplit is: ', bestCentToSplit)\n",
    "        print('the len of bestClustAss is: ', len(bestClustAss))\n",
    "        centList[bestCentToSplit] = bestNewCents[0, :].tolist()[0]  # replace a centroid with two best centroids\n",
    "        centList.append(bestNewCents[1, :].tolist()[0])\n",
    "        clusterAssment[nonzero(clusterAssment[:, 0].A == bestCentToSplit)[0],:] = bestClustAss  # reassign new clusters, and SSE\n",
    "    return mat(centList), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#practice example\n",
    "#distance calc function：结合两个点经纬度（用角度做单位），返回地球表面两点之间距离\n",
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)\n",
    "    b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#draw function\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def clusterClubs(numClust=5):#参数：希望得到的簇数目\n",
    "    datList = []\n",
    "    for line in open('places.txt').readlines():#获取地图数据\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])#逐个获取第四列和第五列的经纬度信息\n",
    "    datMat = mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    #draw\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]#创建矩形\n",
    "    #创建不同标记图案\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('Portland.png')#导入地图\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculate Euclidean distance\n",
    "def euclDistance(vector1, vector2):\n",
    "    return sqrt(sum(power(vector2 - vector1, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# init centroids with random samples\n",
    "def initCentroids(dataSet, k):\n",
    "    numSamples, dim = dataSet.shape\n",
    "    centroids = zeros((k, dim))\n",
    "    for i in range(k):\n",
    "        index = int(random.uniform(0, numSamples))\n",
    "        centroids[i, :] = dataSet[index, :]\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# k-means cluster\n",
    "def kmeans2(dataSet, k):\n",
    "    numSamples = dataSet.shape[0]\n",
    "    # first column stores which cluster this sample belongs to,\n",
    "    # second column stores the error between this sample and its centroid\n",
    "    clusterAssment = mat(zeros((numSamples, 2)))\n",
    "    clusterChanged = True\n",
    "\n",
    "    ## step 1: init centroids\n",
    "    centroids = initCentroids(dataSet, k)\n",
    "\n",
    "    while clusterChanged:\n",
    "        clusterChanged = False\n",
    "        ## for each sample\n",
    "        for i in range(numSamples):\n",
    "            minDist  = 100000.0\n",
    "            minIndex = 0\n",
    "            ## for each centroid\n",
    "            ## step 2: find the centroid who is closest\n",
    "            for j in range(k):\n",
    "                distance = euclDistance(centroids[j, :], dataSet[i, :])\n",
    "                if distance < minDist:\n",
    "                    minDist  = distance\n",
    "                    minIndex = j\n",
    "\n",
    "            ## step 3: update its cluster\n",
    "            if clusterAssment[i, 0] != minIndex:\n",
    "                clusterChanged = True\n",
    "                clusterAssment[i, :] = minIndex, minDist**2\n",
    "\n",
    "        ## step 4: update centroids\n",
    "        for j in range(k):\n",
    "            pointsInCluster = dataSet[nonzero(clusterAssment[:, 0].A == j)[0]]\n",
    "            centroids[j, :] = mean(pointsInCluster, axis = 0)\n",
    "\n",
    "    print ('Congratulations, cluster complete!')\n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# show your cluster only available with 2-D data\n",
    "def showCluster(dataSet, k, centroids, clusterAssment):\n",
    "    numSamples, dim = dataSet.shape\n",
    "    if dim != 2:\n",
    "        print (\"Sorry! I can not draw because the dimension of your data is not 2!\")\n",
    "        return 1\n",
    "\n",
    "    mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']\n",
    "    if k > len(mark):\n",
    "        print (\"Sorry! Your k is too large! please contact Zouxy\")\n",
    "        return 1\n",
    "\n",
    "    # draw all samples\n",
    "    for i in range(numSamples):\n",
    "        markIndex = int(clusterAssment[i, 0])\n",
    "        plt.plot(dataSet[i, 0], dataSet[i, 1], mark[markIndex])\n",
    "\n",
    "    mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']\n",
    "    # draw the centroids\n",
    "    for i in range(k):\n",
    "        plt.plot(centroids[i, 0], centroids[i, 1], mark[i], markersize = 12)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[4.838138]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##P187 K均值聚类 \n",
    "datMat=mat(loadDataSet('testSet.txt'))\n",
    "\n",
    "min(datMat[:,0])\n",
    "min(datMat[:,1])\n",
    "max(datMat[:,1])\n",
    "max(datMat[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.184632816681332"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "randCent(datMat, 2)\n",
    "distEclud(datMat[0], datMat[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-3.53973889, -2.89384326],\n",
       "        [ 2.65077367, -2.79019029],\n",
       "        [ 2.6265299 ,  3.10868015],\n",
       "        [-2.46154315,  2.78737555]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## P189 改进的算法\n",
    "##reload(kMeans)\n",
    "datMat=mat(loadDataSet('testSet.txt'))\n",
    "myCentroids, clustAssing = kMeans(datMat,4)\n",
    "myCentroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sseSplit, and notSplit:  570.7227574246755 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "sseSplit, and notSplit:  68.68654812621844 38.06295063565756\n",
      "sseSplit, and notSplit:  21.290859679422137 532.6598067890178\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.93386365,  3.12782785],\n",
       "        [-2.94737575,  3.3263781 ],\n",
       "        [-0.45965615, -2.7782156 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##P190二分K-均值聚类算法 \n",
    "##reload(kMeans)\n",
    "datMat3=mat(loadDataSet('testSet2.txt'))\n",
    "centList,myNewAssments=biKmeans(datMat3,3)\n",
    "centList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 1: load data...\n"
     ]
    }
   ],
   "source": [
    "##KMeans 例子2\n",
    "\n",
    "#import kMeans1\n",
    "## step 1: load data\n",
    "print (\"step 1: load data...\")\n",
    "dataSet = []\n",
    "fileIn = open('testSet.txt')\n",
    "for line in fileIn.readlines():\n",
    "    lineArr = line.strip().split('\\t')\n",
    "    dataSet.append([float(lineArr[0]), float(lineArr[1])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 2: clustering...\n",
      "Congratulations, cluster complete!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "## step 2: clustering...\n",
    "print (\"step 2: clustering...\")\n",
    "dataSet = mat(dataSet)\n",
    "k = 4\n",
    "centroids, clusterAssment = kmeans2(dataSet, k)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 3: show the result...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd605969828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## step 3: show the result\n",
    "print (\"step 3: show the result...\")\n",
    "showCluster(dataSet, k, centroids, clusterAssment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#paint为笔者自己写的绘图函数\n",
    "def paint(xArr,yArr,xArr1,yArr1):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.scatter(xArr,yArr,c='blue')\n",
    "    ax.scatter(xArr1,yArr1,c='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sseSplit, and notSplit:  541.2976292649145 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "sseSplit, and notSplit:  25.535514707587865 501.7683305828214\n",
      "sseSplit, and notSplit:  67.2202000797829 39.52929868209309\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  40\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-0.45965615 -2.7782156 ]\n",
      " [-2.94737575  3.3263781 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd605969780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datMat3 = mat(loadDataSet('testSet2.txt'))\n",
    "centList,myNewAssments = biKmeans(datMat3,3)\n",
    "print(centList)\n",
    "xArr = datMat3[:,0].flatten().A[0]\n",
    "yArr = datMat3[:,1].flatten().A[0]\n",
    "xArr1 = centList[:,0].flatten().A[0]\n",
    "yArr1 = centList[:,1].flatten().A[0]\n",
    "\n",
    "paint(xArr,yArr,xArr1,yArr1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 对地理坐标进行聚类\n",
    "#https://blog.csdn.net/weixin_40548136/article/details/86769633\n",
    "import urllib\n",
    "import json\n",
    "def geoGrab(stAddress, city):\n",
    "    '''从Yahoo返回一个字典'''\n",
    "    apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder\n",
    "    params = {}\n",
    "    params['flags'] = 'J'#JSON return type\n",
    "    params['appid'] = 'aaa0VN6k'\n",
    "    params['location'] = '%s %s' % (stAddress, city)\n",
    "    url_params = urllib.parse.urlencode(params)\n",
    "    yahooApi = apiStem + url_params      #print url_params\n",
    "    print(yahooApi)\n",
    "    c=urllib.request.urlopen(yahooApi)\n",
    "    return json.loads(c.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "def massPlaceFind(fileName):\n",
    "    '''将数据封装并且保存到文件中'''\n",
    "    fw = open('places2.txt', 'w')\n",
    "    for line in open(fileName).readlines():\n",
    "        line = line.strip()\n",
    "        lineArr = line.split('\\t')\n",
    "        retDict = geoGrab(lineArr[1], lineArr[2])\n",
    "        if retDict['ResultSet']['Error'] == 0:\n",
    "            lat = float(retDict['ResultSet']['Results'][0]['latitude'])\n",
    "            lng = float(retDict['ResultSet']['Results'][0]['longitude'])\n",
    "            print(\"%s\\t%f\\t%f\" % (lineArr[0], lat, lng))\n",
    "            fw.write('%s\\t%f\\t%f\\n' % (line, lat, lng))\n",
    "        else: print(\"error fetching\")\n",
    "        sleep(1)\n",
    "    fw.close()\n",
    "    \n",
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)\n",
    "    b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * \\\n",
    "                      cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('Portland.png')\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sseSplit, and notSplit:  3043.2633158892286 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  69\n",
      "sseSplit, and notSplit:  1321.0446323852361 851.4388885907233\n",
      "sseSplit, and notSplit:  505.6196081746114 2191.824427298505\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  39\n",
      "sseSplit, and notSplit:  245.68445200823226 1594.3600704108153\n",
      "sseSplit, and notSplit:  718.638653214429 1321.0446323852361\n",
      "sseSplit, and notSplit:  410.1002223762699 1429.5623391558677\n",
      "the bestCentToSplit is:  2\n",
      "the len of bestClustAss is:  24\n",
      "sseSplit, and notSplit:  245.68445200823226 1261.5391109669933\n",
      "sseSplit, and notSplit:  464.720598318257 988.2236729414145\n",
      "sseSplit, and notSplit:  0.0 1839.6625615321377\n",
      "sseSplit, and notSplit:  126.75974272895348 1429.5623391558677\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lab466/anaconda3/lib/python3.6/site-packages/numpy/matrixlib/defmatrix.py:536: RuntimeWarning: Mean of empty slice.\n",
      "  return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)\n",
      "/home/lab466/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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3DKvdq/fifRpHB2xlfG7fuYuiKDiOw3PPPhmWyBpTZEliZ2eD2lEDXdcWavNb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LcOQmxyN1E2UDYAZQM4eDaoaVyclPI2cMb++EjF/4aBr1WUTQCfqjjMVOW+CoVC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6036f0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
